How to decide? Different methods of calculating gene expression from short oligonucleotide array data will give different results
about
Algorithm-driven artifacts in median polish summarization of microarray dataGenetic subtraction profiling identifies genes essential for Arabidopsis reproduction and reveals interaction between the female gametophyte and the maternal sporophyteCardiovascular genomics: a biomarker identification pipelineA Web-based and Grid-enabled dChip version for the analysis of large sets of gene expression dataArrayWiki: an enabling technology for sharing public microarray data repositories and meta-analysesA Four-Biomarker Blood Signature Discriminates Systemic Inflammation Due to Viral Infection Versus Other Etiologies.Activation of MAPK pathways links LMNA mutations to cardiomyopathy in Emery-Dreifuss muscular dystrophy.Accurate data processing improves the reliability of Affymetrix gene expression profiles from FFPE samplesComparison of gene expression microarray data with count-based RNA measurements informs microarray interpretation.Analysis of probe level patterns in Affymetrix microarray data.Phylogenetic modeling of heterogeneous gene-expression microarray data from cancerous specimens.Probe set algorithms: is there a rational best bet?AffyMiner: mining differentially expressed genes and biological knowledge in GeneChip microarray data.Transcriptional profiling of C57 and DBA strains of mice in the absence and presence of morphine.Identification of a predictive gene expression signature of cervical lymph node metastasis in oral squamous cell carcinoma.A meta-analysis of kidney microarray datasets: investigation of cytokine gene detection and correlation with rt-PCR and detection thresholdsEffects on linkage analyses of different Affymetrix expression measures as quantitative trait phenotypes.Comparative gene expression profiles between heterotic and non-heterotic hybrids of tetraploid Medicago sativaAnalyzing gene expression data from microarray and next-generation dna sequencing transcriptome profiling assays using GeneSifter analysis edition.An oligo-based microarray offers novel transcriptomic approaches for the analysis of pathogen resistance and fruit quality traits in melon (Cucumis melo L.).Prioritizing genes for follow-up from genome wide association studies using information on gene expression in tissues relevant for type 2 diabetes mellitusProbe set filtering increases correlation between Affymetrix GeneChip and qRT-PCR expression measurements.SWISS MADE: Standardized WithIn Class Sum of Squares to evaluate methodologies and dataset elements.A robust method for estimating gene expression states using Affymetrix microarray probe level dataDynamics of dendritic cell maturation are identified through a novel filtering strategy applied to biological time-course microarray replicatesA Brassica exon array for whole-transcript gene expression profiling.Differential expression of decorin, EGFR and cyclin D1 during mammary gland carcinogenesis in TA2 mice with spontaneous breast cancerRefGenes: identification of reliable and condition specific reference genes for RT-qPCR data normalization.Downstream targets of HOXB4 in a cell line model of primitive hematopoietic progenitor cellsActivation of MAPK in hearts of EMD null mice: similarities between mouse models of X-linked and autosomal dominant Emery Dreifuss muscular dystrophy.Stem cell-like gene expression in ovarian cancer predicts type II subtype and prognosisExperimental design, preprocessing, normalization and differential expression analysis of small RNA sequencing experiments.How cyanobacteria pose new problems to old methods: challenges in microarray time series analysis.Genes and biochemical pathways in human skeletal muscle affecting resting energy expenditure and fuel partitioningIdentification of the IGF1/PI3K/NF κB/ERK gene signalling networks associated with chemotherapy resistance and treatment response in high-grade serous epithelial ovarian cancerA revised design for microarray experiments to account for experimental noise and uncertainty of probe responsePolymorphisms in predicted miRNA binding sites and osteoporosis.Peripheral blood monocyte-expressed ANXA2 gene is involved in pathogenesis of osteoporosis in humansIdentification of the genetic basis for complex disorders by use of pooling-based genomewide single-nucleotide-polymorphism association studiesExon arrays provide accurate assessments of gene expression
P2860
Q21284343-923C3A5A-7CE3-41CF-8750-3E464046DA17Q21999705-116D4F11-845F-4F4B-B0BB-416C577AA627Q28596902-C5B90B2F-46E6-4C8D-B4B6-93ED08B21241Q28756367-B2188BE3-70B7-4CFB-B87B-760CDE13FF85Q28757925-366A546C-AB78-46BD-902E-8146D34F1E19Q30250910-B20F3F7F-8DA3-4335-B7C6-7F57124A0C44Q30498939-BF048D94-F0CA-4027-84A7-4F53BE4DAA74Q30747169-F7C7BF3F-EB11-4E70-BD20-AC7770F52BA9Q30840618-A950069C-335C-4165-B4A8-4640D90847DCQ31111668-CF0178C2-C9B6-4778-AD7B-3472E44D6A50Q31171461-37F21D83-D395-450B-A798-C5E3B9174027Q33255894-7E22CBA7-68A6-4EB8-868A-A22E520DC4FEQ33268938-48D53697-15F2-4BDA-81D7-27A8B249929DQ33279171-CC6C79D4-9F45-47DF-AA75-AE73A736C74FQ33280286-58ABBD19-C03F-4284-A3F0-CD566DB07219Q33280652-DC8E85DE-8681-41AA-886B-686B6CAF942BQ33332876-7522453B-61AE-46B7-90D8-E4BDB658C6E7Q33493208-F0462764-DBEC-42A9-B658-4A61D2406035Q33499802-8A2521A8-A827-40BC-B61C-20F707F2699DQ33509897-D8A143AE-3FE7-4586-97C0-36E11AC5EA11Q33521532-ECA8263D-31E4-45D3-9442-F77452947E4AQ33534022-2C1530B2-9965-475D-87D1-F626F081FA44Q33548674-7D0C7408-0377-4E56-BE45-34E534FDFBC4Q33551524-1A87D83A-CD52-4561-BBCF-1AD2D2CE8F53Q33648536-75399E97-36DF-426E-8492-AA7D0409CF09Q33700501-C408C1B9-BFBB-4425-9D4C-B8331BDC8738Q33753455-04047E66-2311-4EC0-A11D-1BE3CBB723EFQ33850341-031BA946-47BD-4B8C-A835-4FC65D902A87Q34052591-6A6028ED-04BC-4789-9C20-E8D5D9A7F543Q34580311-62C033A1-2C74-4DDC-9363-2580A6FC543EQ34639023-DD7EFFB3-00B9-4ADA-8795-5153B7045742Q34662492-67B59104-7133-4084-9CE0-CF55C9B6611EQ34677870-CD977ADE-0ABD-48C8-8C2A-C2676D453076Q34760282-5C7AE946-3FF8-4B40-B7E8-846637565A57Q35044404-DB324AF6-46B0-401A-B08E-1A407A794FA1Q35117976-D8098ECE-C31B-4FFF-B432-FB436C7B531CQ35229951-AD54DC70-E005-47A2-9B35-A6F5EFF648B4Q35579638-51F8E655-B2C5-4299-BCEA-6489CB4ADFFEQ35616540-8D1EFDEA-14C4-4E9D-BD1F-4D7315BA2185Q35906488-5CD35BD4-976D-47AC-86C8-2C3B1EC3BAC7
P2860
How to decide? Different methods of calculating gene expression from short oligonucleotide array data will give different results
description
2006 nî lūn-bûn
@nan
2006 թուականի Մարտին հրատարակուած գիտական յօդուած
@hyw
2006 թվականի մարտին հրատարակված գիտական հոդված
@hy
2006年の論文
@ja
2006年論文
@yue
2006年論文
@zh-hant
2006年論文
@zh-hk
2006年論文
@zh-mo
2006年論文
@zh-tw
2006年论文
@wuu
name
How to decide? Different metho ...... ta will give different results
@ast
How to decide? Different metho ...... ta will give different results
@en
type
label
How to decide? Different metho ...... ta will give different results
@ast
How to decide? Different metho ...... ta will give different results
@en
prefLabel
How to decide? Different metho ...... ta will give different results
@ast
How to decide? Different metho ...... ta will give different results
@en
P2093
P2860
P356
P1433
P1476
How to decide? Different metho ...... ta will give different results
@en
P2093
Anton J M Peeters
Frank F Millenaar
John Okyere
Laurentius A C J Voesenek
Martijn van Zanten
P2860
P2888
P356
10.1186/1471-2105-7-137
P50
P577
2006-03-15T00:00:00Z
P5875
P6179
1021178486